Shuxin Zheng

According to our database, Shuxin Zheng authored at least 36 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning.

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao

Nat. Comput. Sci., 2024

Towards Generalist Prompting for Large Language Models by Mental Models.

Haoxiang Guan, Jiyan He, Shuxin Zheng, En-Hong Chen, Weiming Zhang, Nenghai Yu

CoRR, 2024

A Cross-Period Network for Clothing Change Person Re-Identification.

Shuxin Zheng, Sai Liang, Chunyun Meng, Zhongguo Zhang, Li Luan

IEEE Access, 2024

2023

Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi'an international tourist arrivals.

Shuxin Zheng, Zhongguo Zhang

PeerJ Comput. Sci., 2023

Invertible Rescaling Network and Its Extensions.

Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu

Int. J. Comput. Vis., 2023

Control Risk for Potential Misuse of Artificial Intelligence in Science.

Jiyan He, Weitao Feng, Yaosen Min, Jingwei Yi, Kunsheng Tang, Shuai Li, Jie Zhang, Kejiang Chen, Wenbo Zhou, Xing Xie, Weiming Zhang, Nenghai Yu, Shuxin Zheng

CoRR, 2023

Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling.

Yiwen Zheng, Prakash Thakolkaran, Jake A. Smith, Ziheng Lu, Shuxin Zheng, Bichlien H. Nguyen, Siddhant Kumar, Aniruddh Vashisth

CoRR, 2023

M-OFDFT: Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning.

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao

CoRR, 2023

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning.

Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu

CoRR, 2023

Molecule Generation For Target Protein Binding with Structural Motifs.

Zaixi Zhang, Yaosen Min, Shuxin Zheng, Qi Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

One Transformer Can Understand Both 2D & 3D Molecular Data.

Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A New Enhanced Tensor Low Rank Representation Method for Image Denoising.

Cuicui Peng, Yunjie Zhang, Shuxin Zheng

Proceedings of the 9th International Conference on Communication and Information Processing, 2023

2022

Predicting the protein-ligand affinity from molecular dynamics trajectories.

Yaosen Min, Ye Wei, Peizhuo Wang, Nian Wu, Stefan Bauer, Shuxin Zheng, Yu Shi, Yingheng Wang, Dan Zhao, Ji Wu, Jianyang Zeng

CoRR, 2022

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets.

Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu

CoRR, 2022

Quantized Training of Gradient Boosting Decision Trees.

Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Your Transformer May Not be as Powerful as You Expect.

Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Hybrid Medical Causal Inference Platform Based on Data Lake.

Peng Ren, Xingyue Liu, Shuxin Zheng, Lijun Liao, Xin Li, Ligong Lu, Xia Wang, Ruoyu Wang, Ming Sheng

Proceedings of the Health Information Science - 11th International Conference, 2022

2021

First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track.

Chengxuan Ying, Mingqi Yang, Shuxin Zheng, Guolin Ke, Shengjie Luo, Tianle Cai, Chenglin Wu, Yuxin Wang, Yanming Shen, Di He

CoRR, 2021

Do Transformers Really Perform Bad for Graph Representation?

Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu

CoRR, 2021

Revisiting Language Encoding in Learning Multilingual Representations.

Shengjie Luo, Kaiyuan Gao, Shuxin Zheng, Guolin Ke, Di He, Liwei Wang, Tie-Yan Liu

CoRR, 2021

Do Transformers Really Perform Badly for Graph Representation?

Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding.

Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Open Catalyst Challenge 2021: Competition Report.

Abhishek Das, Muhammed Shuaibi, Aini Palizhati, Siddharth Goyal, Aditya Grover, Adeesh Kolluru, Janice Lan, Ammar Rizvi, Anuroop Sriram, Brandon M. Wood, Devi Parikh, Zachary W. Ulissi, C. Lawrence Zitnick, Guolin Ke, Shuxin Zheng, Yu Shi, Di He, Tie-Yan Liu, Chengxuan Ying, Jiacheng You, Yihan He, Rostislav Grigoriev, Ruslan Lukin, Adel Yarullin, Max Faleev

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

How could Neural Networks understand Programs?

Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, Tie-Yan Liu

Proceedings of the 38th International Conference on Machine Learning, 2021

Cross-Iteration Batch Normalization.

Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, Stephen Lin

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Modeling Lost Information in Lossy Image Compression.

Yaolong Wang, Mingqing Xiao, Chang Liu, Shuxin Zheng, Tie-Yan Liu

CoRR, 2020

MC-BERT: Efficient Language Pre-Training via a Meta Controller.

Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, Tie-Yan Liu

CoRR, 2020

On Layer Normalization in the Transformer Architecture.

Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu

Proceedings of the 37th International Conference on Machine Learning, 2020

Invertible Image Rescaling.

Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

OptQuant: Distributed training of neural networks with optimized quantization mechanisms.

Li He, Shuxin Zheng, Wei Chen, Zhiming Ma, Tie-Yan Liu

Neurocomputing, 2019

G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space.

Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Nenghai Yu, Tie-Yan Liu

Proceedings of the 7th International Conference on Learning Representations, 2019

Capacity Control of ReLU Neural Networks by Basis-Path Norm.

Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, Nenghai Yu, Tie-Yan Liu

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Optimizing Neural Networks in the Equivalent Class Space.

Qi Meng, Wei Chen, Shuxin Zheng, Qiwei Ye, Tie-Yan Liu

CoRR, 2018

A Dendritic Neuron Model with Nonlinearity Validation on Istanbul Stock and Taiwan Futures Exchange Indexes Prediction.

Dongbao Jia, Shuxin Zheng, Lin Yang, Yuki Todo, Shangce Gao

Proceedings of the 5th IEEE International Conference on Cloud Computing and Intelligence Systems, 2018

2017

Asynchronous Stochastic Gradient Descent with Delay Compensation.

Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhiming Ma, Tie-Yan Liu

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Asynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning.

Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhiming Ma, Tie-Yan Liu

CoRR, 2016